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Open Access Highly Accessed Research article

Developing a dashboard to help measure and achieve the triple aim: a population-based cohort study

Hsien-Yeang Seow1* and Lyn M Sibley2

Author Affiliations

1 Cancer Care Ontario Research Chair in Health Services Research, Department of Oncology, Centre for Health Economics and Policy Analysis, McMaster University, 699 Concession St, 4th Fl, Rm 4-229, Hamilton L8V 5C2, Ontario, Canada

2 Primary Care and Population Health Division, Institute of Clinical Evaluative Sciences, Bldg G1-06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada

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BMC Health Services Research 2014, 14:363  doi:10.1186/1472-6963-14-363

Published: 30 August 2014

Abstract

Background

Health system planners aim to pursue the three goals of Triple Aim: 1) reduce health care costs; 2) improve population health; and 3) improve the care experience. Moreover, they also need measures that can reliably predict future health care needs in order to manage effectively the health system performance. Yet few measures exist to assess Triple Aim and predict future needs at a health system level. The purpose of this study is to explore the novel application of a case-mix adjustment method in order to measure and help improve the Triple Aim of health system performance.

Methods

We applied a case-mix adjustment method to a population-based analysis to assess its usefulness as a measure of health system performance and Triple Aim. The study design was a retrospective, cohort study of adults from Ontario, Canada using administrative databases: individuals were assigned a predicted illness burden score using a case-mix adjustment system from diagnoses and health utilization data in 2008, and then followed forward to assess the actual health care utilization and costs in the following year (2009). We applied the Johns Hopkins Adjusted Clinical Group (ACG) Case-Mix System to categorize individuals into 60 levels of healthcare need, called ACGs. The outcomes were: 1) Number of individuals per ACG; 2) Total system costs per ACG; and 3) Mean cost per person per ACG, which together formed a health system “dashboard”.

Results

We identified 11.4 million adults. 16.1% were aged 65 or older, 3.2 million (28%) did not use health care services that year, and 45,000 (0.4%) were in the highest acuity ACG category using 12 times more than an average adult. The sickest 1%, 5% and 15% of the population use about 10%, 30% and 50% of total health system costs respectively. The dashboard measures 2 dimensions of Triple Aim: 1) reduced costs: when total system costs per ACG or when average costs per person is reduced; and 2) improved population health: when more people move into healthier rather than sicker ACGs. It can help to achieve the third aim, improved care experience, when ACG utilization predictions are reported to providers to proactively develop care plans.

Conclusions

The dashboard, developed via case-mix methods, measures 2 of the Triple Aim goals and can help health system planners better manage their health delivery systems.

Keywords:
Healthcare quality indicators; Quality improvement; Healthcare quality assurance; Risk adjustment; Canada